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Working Paper Series 2016/59/STR/IIPI
(Revised version of 2015/66/EPS/IIPI)
A Working Paper is the author’s intellectual property. It is intended as a means to promote research to interested readers. Its content should not be copied or hosted on any server without written permission from [email protected] Find more INSEAD papers at http://www.insead.edu/facultyresearch/research/search_papers.cfm
Mapping the Business Systems of 61 Major Economies:
A Taxonomy and Implications for Varieties of Capitalism and Business Systems Research
Michael A. Witt
INSEAD, [email protected] (Corresponding author)
Luiz Ricardo Kabbach de Castro
EESC - Universidade de São Paulo, [email protected]
Kenneth Amaeshi University of Edinburgh Business School, [email protected]
Sami Mahroum
INSEAD, [email protected]
Dorothee Bohle Central European University, [email protected]
Lawrence Saez
School of Oriental and African Studies (SOAS), [email protected] Efforts to build a universal theory of the world’s business systems require empirical grounding in an understanding of the variety that need explaining. To support such theorizing, we analyzed the institutional structures of 61 major economies, accounting for 93.5 percent of 2013 world GDP (PPP). We found nine main types of business systems: Collaborative, Coordinated Market, Liberal Market, European Peripheral, Advanced Emerging, Advanced City, Arab Oil-Based, Emerging, and Socialist Economies. Our findings suggest empirical support for the CME vs. LME dichotomy for part of the OECD; identify some of the business systems proposed recently as sub-types of larger clusters; indicate that institutional diversity seems to increase with development level; and cast doubt on the notions of state-led and family-led capitalism as types of business systems. Keywords: Varieties of Capitalism; Africa; Asia; Central and Eastern Europe; Middle East; South America JEL Classification: P1, P2, P50
Electronic copy available at: http://ssrn.com/abstract=2660126
1. Introduction
What are the world’s main types of business systems,1 and what are their characteristics? The
answer to this question is of great relevance not only to multinational enterprises grappling with
varying rules of the game in different countries. It is also a prerequisite for the building of a
general theory of varieties of business systems in the world, which in turn would enable
theorizing about the implications of these varieties for economic and political outcomes such as
wealth generation and distribution or comparative advantages. Such theory can only emerge
when it is clear what it needs to explain (Starbuck, 1993; Weick, 1995; Swedberg, 2014) – that is,
when the gamut of business systems in the world economy is known.
Recent years have seen the literature move towards a more encompassing understanding
of business systems. Whitley’s (1999) seminal work on business systems focused essentially on
the OECD and Northeast Asia, while Hall and Soskice’s Varieties of Capitalism (2001) as well
as Amable’s work (2003) limited their scope to the OECD. Subsequent works have since added
insights on business systems in different regions of the world, including Africa (e.g., Wood and
Frynas, 2006; Amaeshi and Amao, 2009), Asia (e.g., Kim, 2010; Boyer et al., 2012; Kalinowski,
2013; Witt and Redding, 2013; Zhang and Whitley, 2013), Eastern Europe (e.g., Nölke and
Vliegenthart, 2009; Bohle and Greskovits, 2012), and South America (e.g., Schneider, 2009;
Schneider, 2013; Musacchio and Lazzarini, 2014). A growing number of pieces have also
explored categories that span regions, such as those of state capitalism (e.g., Bremmer, 2009;
Nölke, 2010) or of emerging markets (e.g., Fainshmidt et al., forthcoming). The overall result of
these efforts has been a much-improved empirical understanding of the institutional structures of
previously understudied geographies. However, a consolidated overview of the overall
landscape of the business systems in the world economy, and thus a firmer foundation for
theorizing about them, is still absent from the literature.
The objective of this paper is to take a step towards evolving such a firmer foundation.
We propose a first step towards a geographically encompassing taxonomy of the world’s major
business systems and their characteristics. Based on a synthesis of prior research and the in-
depth knowledge of regional experts, we undertake an analysis of the business systems of 61
major economies in the world. Our sample spans all continents save Antarctica, and it accounts
1 We use the terms “business systems” and “varieties of capitalism” interchangeably (for a justification, see Witt &
Redding, 2013).
2
for 93.5 percent of 2013 world GDP, measured at purchasing power parity (PPP) (World Bank,
2015). While our coverage does not reach 100 percent – an unlikely feat for any paper in the
foreseeable future – it seems to us that just as a jigsaw puzzle that is 93.5 percent complete, our
results should provide a reasonable approximation of the overall picture.
Our analysis identified nine main types of business systems. Three of these include
economies from at least two continents, which suggests the presence of underlying drivers other
than geography. Our findings have a number of potentially important implications for the field.
Among others, they underline the validity of the CME vs. LME dichotomy for parts of the
OECD, but not the rest of the world; identify some of the labels proposed in recent years as valid
but sub-types of larger clusters; find high institutional similarity among most emerging markets
as well as advanced emerging markets on the one hand and institutional diversity of advanced
industrialized countries on the other; and call into doubt the notions of state-led and family-led
capitalism as business systems.
In the sections that follow, we first lay the groundwork by identifying our general
epistemological approach in the context of the existing literature. We then explain the
dimensions of comparison we employed in this paper, our data, and our methods before
presenting our results and discussing the characteristics of the clusters we identified. We then
spell our possible implications of our findings for taxonomizing the world’s business systems
before closing with a discussion of limitations and attendant potential for future research.
2. Motivation and Epistemology
We assume that it is desirable to develop a general theory of the world’s business systems, where
“theory” is defined as a “systematic [set] of interrelated statements intended to explain some
aspect of social life” (Babble, 2012: 44). Fully developed, such a theory would probably spell
out mechanisms to account for the origins of the business systems we observe; their likely
trajectories over time, also in response to external shocks; and their impact on economic,
political, and social factors such as economic growth, quality of life, inequality, and sectoral
strengths and weaknesses. To the extent multiple mechanisms are at work, such theory would
ideally also lay out the contingencies under which individual mechanisms are (de)activated.
The field has already evolved important theoretical insights about all aspects of our ideal-
typical theory. For instance, a number of works have put the evolution of varieties of capitalism
3
in historical perspective and identified drivers leading to the present diversity among advanced
industrialized economies (Streeck and Yamamura, 2001; Thelen, 2004; Hancké et al., 2007;
Cusack et al., 2010). In terms of evolution over time, research has explored the question of
convergence (Djelic, 1998; Whitley, 1999; Deakin et al., forthcoming), but also an apparent
trend towards liberalization (Thelen, 2014; Van der Zwan, 2014) and institutional adaptability
more generally (Streeck and Thelen, 2005; Vogel, 2006; Witt, 2006; Hall and Thelen, 2009;
Jackson and Deeg, 2012). And with regard to outcomes, the literature has examined the linkages
between varieties of capitalism and results such as comparative advantages (Hall and Soskice,
2001; Whitley, 2008; Schneider and Paunescu, 2012; Witt and Jackson, forthcoming), innovative
capabilities (Boyer, 2004; Akkermans et al., 2009; Allen, 2013; Keller and Block, 2013;
Boschma and Capone, 2015), CSR (Matten and Moon, 2008; Gjølberg, 2009; Jackson and
Apostolakou, 2010; Brammer et al., 2012; Kang and Moon, 2012) and inequality (Rueda and
Pontusson, 2000; Schneider and Makszin, 2014; Thelen, 2014).
This list is far from exhaustive, and a comprehensive review of these efforts is beyond the
possibilities even of dedicated review papers (Wood et al., 2014), leave alone research papers
such as this one. At the same time, the list does serve to illustrate two larger points. One is that
the field has not succeeded in building a general, unified theory accounting for different aspects
of business systems, such as origins, future trajectories, and effects. At the same time, much
older disciplines, such as physics and economics, have likewise not succeeded in building
unified theories. The second is that most of the leading theoretical works in our field have been
built around the specific context of the West plus Japan. There is no doubt that these economies
and a better understanding of how they work are important. However, this partial approach it is
a problem from the perspective of building the overall theory we outlined earlier in that, in all
probability, much of these theories is geographically contingent.
A key challenge in this context has been that we know much too little about the
institutional characteristics of the rest of the world. A comprehensive theory of the kind we
introduced earlier cannot emerge unless it is clear what empirical realities it needs to explain
(Starbuck, 1993; Weick, 1995; Swedberg, 2014). As Swedberg (2014: 10) summarized, “you
cannot theorize without having something to theorize about; and this something you have to
acquire through observation.” Starbuck (1993) and Weick (1995) make the same point,
underscoring the importance of understanding the phenomenon at hand, as expressed in data, as a
4
step towards devising theory. It is then the immersion in these data that allow theories to emerge
(Swedberg, 2014), through processes such as abduction (Peirce, 1957). Building out the theory
itself is likely to be a protracted process (Sutton and Staw, 1995; Weick, 1995; Swedberg, 2014)
that is beyond the scope of this paper.
The first precondition for theorizing about the world’s business systems is thus the
availability of an empirical understanding of what needs explaining. As mentioned in the
introduction, a growing number of works has begun to evolve the requisite information, albeit
usually in a piecemeal fashion. Efforts have also been underway to categorize the empirical
insights thus gained into typologies. While a necessary step in making sense of the data, this has
created challenges in its own. In particular, different labels are in use for different categories of
various overlap. This problem has been with us since the early days, with LMEs (Dore, 2000;
Hall and Soskice, 2001), fragmented business systems (Whitley, 1999) and market based
capitalism (Amable, 2003) arguably referring to the same set of economies. In addition, since
most of the empirical work continues to be geographically organized, it is not necessarily clear
whether a given type is regionally confined or part of a larger, globally present pattern. For
instance, are there economies in both Europe and Asia that belong to the same, “state-led”
variety of capitalism? Proponents of state capitalism would say yes, others (e.g., Witt and
Redding, 2013) suggest the answer is no.
As a result, the overall lay of the land of the world’s business systems – which economies
have similar structures, and how many types actually exist? – remains unclear. The objective of
this paper is to help remedy this situation by taking the next step in understanding the data:
presenting a typology of the world’s major business systems.
3. The world’s business systems: a comparison
We generalize the approach taken by Witt and Redding (2013) in their exploration of Asian
business systems to gain a comprehensive overview of the types of business systems present in
the world’s major economies.
Data
5
Ideally, this study would have included all of the 184 economies presently covered by the World
Bank. In reality, very little is known about the institutional structures of most them, which
limited the range of economies for which we could hope to obtain the requisite in-depth expert
judgment (see below). In selecting our sample, we thus strove to strike a balance between
feasibility in terms of data availability and relevance in terms of coverage of global economic
activity.
We initially selected the 60 largest economies of the world, as measured by GDP at
purchasing power parity (PPP) in 2013, the latest year available at the point of selection (World
Bank, 2015). Each of these economies had a total 2013 PPP GDP of at least US$200 billion, and
collectively, they accounted for 94.7 percent of world GDP. We added to this sample three
smaller economies with a GDP somewhat below US$200 billion, on the grounds that prior
treatment in the literature made their inclusion feasible and relevant: Ireland, New Zealand, and
Slovakia. During data collection, it further became clear that there was insufficient knowledge
about Iran and Iraq, which we had to drop as a result. Our final sample thus encompassed 61
economies, accounting for 93.5 percent of world GDP (Table 1).
*** Table 1 about here ***
For each of these economies, we collected a wide range of institutional data (Table 2).
Our measures and the eight institutional dimensions they represent essentially replicated those
used by Witt and Redding (2013), which combined the key institutional categories identified in
Hall and Soskice (2001), Whitley (1999), Amable (2003), Redding (2005), Hancké, Rhodes, and
Thatcher (2007), and Morgan, Campbell, Crouch, Pedersen, and Whitley (2010). We did,
however, make some minor modifications. Some of these were the result of fine-tuning. For
instance, consultations with experts on business groups, a form of interfirm relations, revealed
that these structures are essentially ubiquitous, with the United States as the key exception. We
thus eliminated this measure and instead followed Schneider et al. (2012) by adding statistics for
mergers and acquisitions as an indicator of interfirm relations.
*** Table 2 about here ***
In other cases, data availability led to changes. For example, union density information
for our full sample was not available. At the same time, the International Trade Union Congress
(2014) has since published a new, comprehensive comparison of trade union rights, which
arguably provides a more meaningful picture of the institutional structure of employment
6
relations than the size of unions alone. Similarly, the United Nations has stopped publishing the
Education Attainment Index, replacing it with the average years of schooling received by adults
and the average years of schooling children can expect to receive. We consequently constructed
a new index by factor-analyzing the two variables.
We further found that some of the economies explored in this paper rely heavily on
foreign capital (e.g., Bohle and Greskovits, 2012). We thus added the stock of inward foreign
direct investment (IFDI) normalized by GDP as a measure in the financial sphere.
As Witt and Redding (2013) discussed, obtaining comparative statistics for institutional
analysis is essentially impossible for many variables of interest. We thus followed their
approach and used statistics where possible and expert qualitative judgment where necessary.
Qualitative judgment for this paper was partially derived from the literature, partially provided
by the authors.2 Specifically, judgments for Anglo-Saxon and Western European economies as
well as for those Asian economies covered by Witt and Redding (2013) were based on the extant
business systems literature. For all other economies, the authors drew on their personal expertise
researching and working in these economies. This process was equivalent to that used by Witt
and Redding (2013), with the key difference that we coded our judgments directly while Witt
and Redding (2013) extracted theirs from country chapters, written by third authors, in the
Oxford Handbook of Asian Business Systems (Witt and Redding, 2014). To reduce the risk of
false assessments, we further consulted with other scholars with relevant regional expertise.
Cluster analysis
To identify the different types of Asian business systems present in our sample, we performed
hierarchical cluster analysis, which has been proposed as an appropriate method to investigate
complex and interrelated dimensions of nations not only as a methodology device, but also as a
foundational tool for sense-making and conceptualization of the object under investigation
(Georgas and Berry, 1995; Ronen and Shenkar, 2013).
We recoded qualitative data in form of dummy variables. For instance, we expressed
differences in employment tenure in three dummy variables, one for short-term, one for medium-
2 The authors of this paper are experts on business systems in different parts of the world. To preserve anonymity in
peer review, we cannot reveal our names to establish our credibility. However, we are known to the editor, who is
in a position to take this information into account in his decision.
7
term, and one for long-term employment. We normalized all measures to eliminate distortions
from differences in numerical magnitude across variables.
We employed hierarchical cluster analysis as implemented in Stata 13.1 (StataCorp,
2013b) using the Gower dissimilarity measure, which is suitable for mixed continuous and
categorical variables as present in our data (StataCorp, 2013a). We chose average linkage (more
specifically, unweighted pair-group method using averages (UPGMA) linkage), which avoids the
tendencies of single linkage and complete linkage to produce extreme results (Greenacre and
Primicerio, 2013).
Given growing interest in viewing varieties of business systems as different sets of
institutional configurations (Jackson, 2005; Kogut and Ragin, 2006; Schneider and Paunescu,
2012), we considered the use of fuzzy-set qualitative comparative analysis (fsQCA) as an
alternative approach. We found the method to be unsuitable because the number of institutional
measures in our dataset is too large for fsQCA to handle. At 48 variables, fsQCA would produce
a truth table of 248
= 281,474,976,710,656 possible configurations. Factor analysis of our
variables identified 11 factors with eigenvalue larger than 1, which would yield 2,048 possible
configurations – still too many. Reducing the number of factors to a manageable number of
about 5, resulting in 32 combinations, would imply the elimination of 6 meaningful factors and
thus a large loss of possibly differentiating information. We consequently decided against the
use of fsQCA.
Figure 1 shows the dendrogram produced by the cluster analysis, and Table 3 presents the
attendant pairwise dissimilarity measures, or “institutional distances” (cf. Jackson and Deeg,
2008).
*** Figure 1 about here ***
*** Table 3 about here ***
To establish how many clusters are actually present in these results, we drew on the
methodological literature on stopping rules, that is, algorithms designed to identify the optimum
number of clusters without the influence of subjective author judgment. Among the many
stopping rules in existence, the methodological literature has identified two as most reliable: the
Calinski-Harabasz pseudo-F index and the Duda-Hart Je(2)/Je(1) index (Milligan and Cooper,
1985; Everitt et al., 2011; StataCorp, 2013a). The literature further suggests that the Calinski-
8
Harabasz stopping rule works better for smaller numbers of clusters in the data, while the Duda-
Hart rule performs better if 4 or more clusters are present (Milligan and Cooper, 1985).
Application of the Calinski-Harabasz stopping rule (StataCorp, 2013a) to our results
suggested the presence of only 4 clusters in the data: (1) 27 emerging markets, shown in the
dendrogram as the branch ranking from ID to CU (read from left to right); (2) a branch mixing
18 advanced emerging markets, Eastern European economies, and Southern European economies,
shown in the dendrogram as ranging from HK to GE; (3) a branch with 6 Anglo-Saxon
economies (GB to IE); and (4) a branch with 10 Northern European economies plus Japan (NO
to JP).
Prior research suggests that this result – 61 economies sorting into only 4 clusters – is
likely to be an underestimate. For instance, Whitley (1999) already identified 6 different types
of business systems present in the advanced industrialized countries plus Northeast Asia. Witt
and Redding (2013) found 7 types of business systems using the advanced industrialized
countries as well as the major Asian economies ranging from India eastwards to Japan. And
exploring a range of emerging economies, Fainshmidt et al. (forthcoming) identified seven types
of clusters. The Calinski-Harabasz rule has a tendency to underestimate the actual number of
clusters in the data once the number clusters becomes large (Milligan and Cooper, 1985), and in
light of the prior evidence, we suspect such underestimation was the case here.
By contrast, the Duda-Hart stopping rule (StataCorp, 2013a) suggested an optimum
number of 9 or 10 clusters. While 9 clusters minimized the Je(2)/Je(1) ratio, the pseudo T-
squared value was minimized for 10 clusters. Either choice was thus methodologically
defensible, and given prior evidence and advantages of the Duda-Hart stopping rule for 4 or
more clusters, likely to be more credible than that produced by the Calinski-Harabasz rule.
To aid us in choosing between 9 or 10 clusters, we conducted a robustness test using
weighted average linkage, which may have advantages for clusters with uneven numbers of
members (Everitt et al., 2011). The resultant dendrogram positions of the 61 economies were
consistent with those produced by average linkage. The Duda-Hart stopping rule suggested the
presence of 9 clusters for those results, suggesting that 9 may indeed be the optimum number.
However, in doing so, it eliminated Japan as a single-member cluster and instead established
Nigeria as a cluster in itself.
9
On balance, we judged the clustering produced by average linkage to be more credible.
Both types of linkages produced an awkward single-member cluster. However, there is evidence
to support this positioning for Japan, which differs from its closets kin, Northern European
economies, in its reliance on micro-corporatist arrangements (Aoki, 1988; Estevez-Abe et al.,
2001; Inagami and Whittaker, 2005). To our knowledge, there is no prior evidence that would
support classifying Nigeria as a unique type of capitalism.
In sum, our analysis suggests the presence of 9 clusters among the 61 economies
explored, as shown in Table 4.
*** Table 4 about here ***
Discussion by cluster
Coordinated and Liberal Market Economies. The economies included in these two clusters are
identical with the groupings proposed by Hall and Soskice (2001), with the exception of Japan.
The replication of these two clusters in our analysis suggests that the original Varieties of
Capitalism (VoC) formulation did succeed in capturing a big divide among the advanced
industrialized economies.
At the same time, it is also clear from Figure 1 that critics of the VoC approach were
correct to point out the existence of important distinctions within these groupings (e.g., Amable,
2003; Campbell and Pedersen, 2007; Hancké et al., 2007). Within the CMEs, for instance, we
see two subgroupings: what one could term “classical CMEs” including Austria, Belgium,
Finland, Germany, the Netherlands, and Sweden; and CMEs with a twist, including Denmark
and Switzerland, which combine features of CMEs with those of LMEs (e.g., Danish “flexicurity”
in employment relations), as well as Norway. We likewise see some variance inside the LME
camp. Ireland stands out as relatively dissimilar, which is consistent with its relatively recent
heritage of corporatism (Ó'Riain, 2014).
The characteristics of these economies have been discussed at length in the literature, so
we will not reiterate them here.
Collaborative Economies. The Duda-Hart stopping rule singled out Japan as a cluster of
its own, though it is closely related to the CMEs. We named this cluster in accordance with
Whitley (1999), who had previously identified Japan as a business system separate from the
10
CMEs. While one can question whether a cluster of one is meaningful as it seems to represent
an exception (albeit an important one) rather than a general pattern, we retained it because doing
otherwise would introduce the precise arbitrariness that stopping rules are intended to prevent.
There is likewise ample literature on the Japanese business system, so we will not enter a
discussion of it here.
European Peripheral Economies. This group comprises the Southern European
economies as well as the Central European economies west of the Ukraine. Inside this cluster,
we find two sub-clusters (Figure 1): the Southern European economies of France, Greece, Italy,
Portugal, and Spain; and the Central European economies of the Czech Republic, Hungary,
Poland, Romania, and Slovakia. This subdivision within this cluster is consistent with their
separate treatment in the literature (e.g., Schmidt, 2002; Amable, 2003; Nölke and Vliegenthart,
2009; Bohle and Greskovits, 2012).
At the same time, important institutional parallels seem to have evolved. These business
systems tend to have3 high levels of general education, long-term average employment tenures in
excess of 10 years, industrial unions with some admixtures of craft unions, bank-led financial
systems mixing market and relationship criteria for credit allocation, and top-down decision-
making inside firms with medium levels of delegation. They also tend to have an important role
for family and some state ownership of firms, about average levels of investor protection,
medium to high rule of the law, welfare state structures with developmental admixtures in the
Central European economies, top-down political governance, and above average levels of voice
and accountability as well as government effectiveness.
Advanced Emerging Economies. This is a geographically heterogeneous group of
emerging economies with relatively high levels of per capita GDP. Inside the cluster, Figure 1
suggests three subgroups: Chile and Turkey, Israel and South Africa, and Korea and Taiwan.
The pairing of Korea and Taiwan is consistent with prior findings of similarities (Witt and
Redding, 2013) on the back of similar paths of economic development drawing on
developmental state policies (e.g., Amsden, 1989; Wade, 1990). Israel and South Africa may
have evolved similarities in the context of international isolation and an active state role in
3 Please note that the list that follows, and similar lists below, indicate general tendencies. Individual economies
may deviate on some dimensions.
11
response to security concerns. Chile and Turkey share long periods of military dominance and
attendant military involvement in the commercial sector.
Common themes in this cluster are decent levels of general education, medium-length
employment tenures, on-the-job and private vocational training, bank-led financial systems
mixing relationships and market criteria in allocating funds, and top-down decision-making
inside the firm. Other common themes include a strong role for family ownership and control
paired with investor protection that is somewhat above average, developmental state policies,
top-down state governance, and, except Turkey, above average institutionalized trust, voice and
accountability, and state effectiveness.
Advanced City Economies. This cluster comprises Hong Kong and Singapore, which are
the only two city-based economies of sufficient size to be included in our sample. Though the
details vary somewhat, both are trade-dependent hub economies with high levels of economic
freedom so as to attract foreign investors. On the back of these strategies, both have attained
very high levels of per capita GDP.
Institutionally, both feature good general education, short-term tenure, private skills
acquisition, predominantly industrial unions with limited rights, bank-led financial systems with
very high levels of inward foreign direct investment and allocation based on market criteria and
relationships. In addition, they show top-down decision-making inside firms with limited
delegation, promotions based on relationships with a performance element, a strong role of
family ownership (and, in Singapore, state ownership), high levels of investor protection,
regulatory states (with developmental elements in Singapore), top-down state decision-making,
and high levels of government effectiveness.
Arab Oil-Based Economies. This group brings together Kuwait, Qatar, Saudi Arabia, and
the United Arab Emirates. Common feature of these economies is the continued importance of
oil production and exports, attempts at diversification into other industries notwithstanding.
Institutional patterns are historically weak but improving education, usually short-term
tenures, the virtual absence of unions and very weak union rights, bank-led financial systems
with low stocks of foreign direct investment and market-based allocation of funds, top-down
decision-making in firms with limited delegation to employees and promotions based on
relationships and performance. Further, there are important roles for family and state ownership
in the economy, poor to average investor protection, above average rule of law, states combining
12
predatory, developmental, and welfare elements, top-down state decision-making, poor voice and
accountability, and average (Kuwait, Saudi Arabia) to good government effectiveness.
Emerging Economies. This cluster is by far the largest cluster with a geographically very
heterogeneous range of economies. Common tendency is the presence of relatively low levels of
per capita GDP, with Russia as an outlier as a result of its revenues derived from resources such
as oil and gas. Inside this cluster, we see a number of sub-clusters, many of them based on
geographic proximity and documented in the literature. For instance, China and Vietnam cluster
together and are in turn relatively similar to India, as established in prior work (Witt and Redding,
2013, 2014). Bangladesh and Pakistan cluster together, which is plausible given that Bangladesh
used to be part of Pakistan, and are themselves similar to India, with which they formed British
India until 1947. Indonesia, the Philippines, and Thailand cluster together, which is again
consistent with prior work (Witt and Redding, 2013). Unsurprising close clustering is further
visible among Colombia, Mexico, and Peru.
But there are also a few surprises. Malaysia forms a sub-cluster with Egypt and Morocco,
Russia with Algeria, Brazil with Kazakhstan, and Argentina with Ukraine. In this context, it is
worth remembering that according to the Duda-Hart stopping rule, these branches do not
constitute valid individual clusters, which implies that the similarities within these sub-clusters
are not much greater than those across them. Given that our knowledge of the institutional
structures of emerging markets is much weaker than that for OECD countries, it is entirely
possible that some of these sub-clusters formed on the basis of measurement error.
The overall clustering, however, shows high levels of consistency across the economies
included in it. General themes include weak past and current education, short-term job tenures,
private skills acquisition, suppressed unions, bank-led finance allocated on the basis of
relationships and state guidance, top-down decision-making inside firms with low delegation and
promotion based on relationships, family and state ownership of firms with often poor investor
protection, low rule of law (except Malaysia), predatory state policies with developmental
admixtures in some cases, top-down state decision-making with generally low levels of voice
and accountability, and poor state effectiveness (again except Malaysia).
Socialist Economies. This last cluster consists of Cuba and Venezuela. Both represent
old-style socialist economies, with Venezuela arguably having regressed to this state under the
rule of Hugo Chavez and his successor. Structurally, these economies feature weak current but
13
decent expected education, weak union rights, bank-led financial systems with very low inward
foreign direct investment and state allocation of funds, top-down decision-making with low
delegation inside firms and promotion based on seniority, state ownership and control of firms
(with a family element in Venezuela), very weak investor protection, very weak rule of law,
predatory state structures, top-down state decision-making, and very poor voice and
accountability as well as state effectiveness.
4. Implications
Our findings have a number of possible implications for our understanding of the world’s
business systems, in particular with respect to existing taxonomies. First, as already briefly
mentioned, the results suggest that Hall and Soskice (2001) did capture an important pattern by
distinguishing CMEs and LMEs, even given the variance within these groups. However, our
findings do not support efforts to apply these categories to economies other than those originally
included in them. They do not travel. Different categories are needed for the field to make sense
of the rest of the world. By extension, this also suggests that the underlying mechanism in the
Varieties of Capitalism approach of institutional complementarities through coherence (cf. Witt
and Jackson, forthcoming) – liberal through for the ideal-typical LME, coordinated for the ideal-
typical CME – is unlikely to extend to the rest of the world.
Second, the results suggest that at least some of the labels used in the field are
empirically valid but, globally speaking, probably part of the same clusters. This effect is most
striking for the Emerging Economies. Prior research has suggested various labels for economies
included in this cluster, such as hierarchical capitalism (Schneider, 2013) and post-socialist
economies and emerging Southeast Asian economies (Witt and Redding, 2013). Each of these
groupings of countries and the labels these prior works have attached to them have some
empirical justification. However, again putting these economies in global perspective and using
statistical methods to identify the most defensible clustering suggests that these and similar
economies all form one large type business system.
One interpretation is that while there are many ways to be a rich economy – CMEs,
LMEs, European Peripheral Economies, and Collaborative Economies – there may be limited
institutional variance among poorer emerging markets. This would be generally consistent with
Gerschenkron’s (1962) observation that late-developing economies leveraged institutional
14
innovations, both relative to other poor economies but also their more advanced counterparts, to
catch up. Path dependency and the absence of a clear trend towards institutional convergence
among the advanced industrialized economies may then explain the persistence of resultant
institutional diversity over time.
While this can account for diversity at the developed end of the spectrum, it leaves open
the question why poorer countries are structurally similar. One interpretation might be that there
is a default mode of organizing societies that have not experienced profound modernization and
attendant institutional and economic development (Giddens and Pierson, 1998; Eisenstadt, 2000;
Beinhocker, 2005). One consistent feature of emerging markets in our sample, for instance, is
the relatively low levels of institutionalized trust, as expressed in the rule of law. In other words,
abstract rules and regulations have not yet superseded the patrimonial structures typical of
traditional societies (Li and Redding, 2014).
At the same time, all emerging markets are clearly not the same. China, for instance, has
done much better economically than, say, Pakistan or Indonesia. More generally, the Emerging
Economies includes both, economies that have seen little development and those that have made
great strides. To the extent the latter draw on institutional innovations as envisioned by
Gerschenkron (1962), this is not (yet?) visible in the data.
One possible interpretation is that the current dimensions for comparing business systems,
as outlined in Table 2, fail to capture these institutional innovations. In other words, this may be
a case of omitted variables, and it seems to us that this question would be a fruitful discussion
topic for the field. It may, however, also be possible that institutional innovation may matter
relatively less for producing growth in economies with lower income levels (Beinhocker, 2005;
Fatás and Mihov, 2009). For instance, the quality of institutions, and thus the role of institutional
innovation, may become relevant only once an economy approaches middle income levels, and
lack of institutional innovation may then lead to stagnation known as the “middle-income trap”
(Redding and Witt, 2007; Lewin et al., 2016; Doner and Schneider, forthcoming). The
development of institutionalized trust (Redding and Witt, 2007) and attendant changes in
governance (Witt, 2016) may be needed to overcome it.
Alternatively, or perhaps in addition, future research may well find that performance
differences within this group may be a function less of the presence or absence of individual
institutional characteristics, but rather of configurations of institutions and their
15
complementarities. Complementarities have occupied a prominent place in the Varieties of
Capitalism literature, where they tend to be equated with coherence across institutional realms,
as already mentioned. However, such coherence is difficult to maintain across multiple realms,
as evident even among the advanced industrialized economies (Witt and Jackson, forthcoming).
Possibly more relevant may thus be Streeck’s (1997) concept of “beneficial constraints,” in
which opposing institutional logics across different institutional realms may produce
complementarities and thus contribute to economic performance. It may thus be acceptable, for
instance, for corruption to be high as long as government effectiveness is such that this private
rent seeking can be channeled to contribute to building up industrial capacity – as was arguably
the case in China for many years. Where such constraint is missing, the proceeds of corruption
may be more likely to materialize on bank accounts in third countries.
We should also acknowledge the influence of other, non-institutional drivers of
performance differences. For instance, the presence of external military threats, ethnic
homogeneity, and the absence of natural resources have all been linked to a higher propensity for
economic development. These non-institutional factors are, strictly speaking, beyond the scope
of business systems analysis. To the extent business systems theory seeks to evolve an
understanding of variety in outcomes, however, these and other factors are likely to need taking
into account.
Similarly, the literature has suggested a distinction between Southern European and
Central European economies. This divide is visible as a sub-cluster in our results, which
provides support for the validity of the respective labels. At the same time, putting these
economies in global perspective and applying statistical procedures for deciding the number of
clusters present in the data, we find that relative to the rest of the world, similarities are such that
both Southern and Central European economies seem to form a single cluster. Much of the
above discussion about Emerging Economies applies here as well, mutatis mutandis. For
instance, it is possible that the common clustering is a result of institutional dimensions that go
beyond those commonly accepted as relevant (Table 2).
Third, the Advanced Emerging Economies combine low geographic proximity with
relatively high institutional similarity. The economies in this group have reached relatively high
levels of per capita GDP and are generally considered exemplars of successful economic
development (stagnant South Africa with its very special economic history being the obvious
16
exception, though recent developments suggest it may be joined by Turkey). This raises the
question whether there may, at least currently, be one particular institutional trajectory towards
high economic development – per capita GDP levels past the middle income trap – for countries
that have neither oil (like the Arab nations) nor are cities (like Hong Kong and Singapore).
While the economies in this cluster had very different institutional starting points, they
apparently converged on similar institutional structures as they got richer. It further suggests that
notions of a geographically based pattern towards development – such as the “Asian
development model” (Kojima, 2000) – may be missing part of the larger picture.
Fourth, our clusters raise questions about the validity of state-led capitalism as a type of
business system. We find economies usually associated with the state-led model distributed
across several clusters: Emerging Economies, Oil-based Arab Economies, Advanced City
Economies, Advanced Emerging Economies, Southern European Economies, and the Socialist
Economies. This is a very large spread of institutional differences that the “state-led” category
would need to accommodate – too large, in our view, to be analytically meaningful.
Recent works on state-led capitalism have recognized this issue and have started to
provide for subcategories (Musacchio et al., 2015). Given the emphasis on ownership patterns,
this may provide for a useful classification with respect to corporate governance outcomes.
However, the underlying logic of state-led capitalism qua business system will remain flawed
because it commits the fallacy of composition: there is a similar role of the state across
economies, thus these economies represent a variety of capitalism. This argument will work
only if one assumes that all the other dimensions of business systems research has identified
(Table 2) are inconsequential, or at least secondary.
The same challenge applies to the newer notion of family-led business systems (Aguilera
et al., 2013; Fainshmidt et al., forthcoming). Economies with important roles for family
business are present in the Emerging Economies, the Advanced City Economies, the Advanced
Emerging Economies of both types, parts of European Peripheral Economies, and the CMEs.
Again, this suggests that the “family-led” category includes too much institutional heterogeneity
to be useful for defining a type of business system, and the same concerns about the underlying
logic as for state capitalism applies.
5. Conclusion and Limitations
17
In this paper, we have undertaken an analysis of the business systems of 61 of the largest
economies in the world, accounting for 93.5 percent of 2013 world GDP at purchasing power
parity. We have found nine main types of business systems in our sample and discussed the
characteristics of each of these clusters as well as the possible implications for the field. Among
others, they underline the validity of the CME vs. LME dichotomy for parts of the OECD, but
not the rest of the world; identify some of the labels proposed in recent years as valid but sub-
types of larger clusters; find high institutional similarity among most emerging markets as well
as advanced emerging markets on the one hand and institutional diversity of advanced
industrialized countries on the other; and call into doubt the notions of state-led and family-led
capitalism as business systems.
Our work, as all others, has limitations. In particular, about half of the data used in the
cluster analysis depended on expert judgment. While we have made great efforts to ensure
accuracy of these judgments, including through triangulation with other experts and, where
possible, the literature, measurement error is bound to have occurred in a number of cases. The
same can be said about the statistics used, with data from less developed economies often having
a greater probability of measurement error even if they are reported by reputable organizations
such as the World Bank.
As a result, it is likely that at least some of the positions identified by our cluster analysis
will be affected by measurement error. This error is likely to be smallest for the OECD countries,
where good data and plentiful prior research have established a fairly clear picture. As a general
rule, the less developed and the less researched a given economy, the higher the risk of
measurement error. Presumably, the risk is greatest for the African and Arab economies in the
sample.
This points to what we believe to be the most important implication for future research
growing out of this discussion. We need better data especially for non-OECD countries to
support expert judgment and, ultimately, to provide usable globally comparative institutional
data. Especially for the less explored geographies covered by our sample – Africa and the
Middle East – the cost/benefit ratio for empirical research seems highly favorable given the very
limited state of our current knowledge. Crucial would also be to obtain some sense of how Iran
and Iraq function. These economies, which are both troubled but fairly large, may well cluster
18
with the Oil-rich Arab Economies. On the other hand, they may not; Iran, for instance, could
present as a separate kind of business system not observed elsewhere.
At the same time, the field needs to be on the lookout for better measures of constructs it
has identified as important. The issue of construct validity in comparative institutional analysis
was already discussed in Witt and Redding (2013), and we would like to second it here. We
overcame some of these issues through expert judgment, but in the end, it would be better to
have hard measures. For instance, given the centrality of corporate funding in our understanding
of business systems, we need to gain a comparative understanding of where firms (not the private
sector, which includes households) get what proportions of their outside funding. Occasionally,
such data become available for select countries (e.g., Witt, 2006). Making this kind of
information available on a broadly comparable basis, and ideally in a time series, would be a
great boon to future research.
Lastly, future research may of course extend the scope to include further countries. In
our view, this would probably be academically interesting but not very substantively meaningful.
Once Iran and Iraq have been brought into the picture, the remaining economies account, at
present, for less than 5 percent of the world economy. All else equal, it would be more fruitful to
put efforts into improving the quality of our measures than to work on including the remaining
small economies.
We began this paper with two questions: What are the world’s main types of business
systems, and what are their characteristics? We hope that with this paper, we have helped shed
some light on these questions and helped motivate further research that will help overcome the
limitations of this study. Comparative institutional analysis has made great strides in the past
two decades, but much work remains yet to be done. The agenda of understanding the world’s
business systems, and attendant efforts at theorizing them, has a long future ahead.
19
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Figure 1 Dendrogram of clusters of business systems among the world’s major economies
27
Table 1 Economies included in this study, by GDP size at purchasing power parity (PPP), 2013. Source: World Bank, 2015
Rank Economy PPP GDP Rank Economy PPP GDP Rank Economy PPP GDP
1 United States 16,768
23 Argentina 928
45 Austria 385
2 China 16,162
24 Poland 911
46 Hong Kong 382
3 India 6,776
25 Egypt 910
47 Romania 379
4 Japan 4,641
26 Pakistan 838
48 Peru 358
5 Germany 3,585
27 Netherlands 778
49 Norway 333
6 Russia 3,460
28 Malaysia 694
50 Czech Republic 303
7 Brazil 3,413
29 South Africa 663
51 Qatar 297
8 France 2,501
30 Philippines 643
52 Kuwait 287
9 UK 2,465
31 Colombia 600
53 Greece 284
10 Indonesia 2,389
32 Venezuela 553
54 Portugal 280
11 Italy 2,130
33 United Arab Emirates 551
55 Israel 264
12 Mexico 2,014
34 Algeria 522
56 Denmark 244
13 South Korea 1,664
35 Iraq* 500
57 Morocco 242
14 Saudi Arabia 1,547
36 Vietnam 475
58 Hungary 220
15 Spain 1,536
37 Belgium 466
59 Cuba 212
16 Canada 1,520
38 Bangladesh 462
60 Finland 208
17 Turkey 1,425
39 Switzerland 457
62 Ireland 199
18 Iran* 1,207
40 Sweden 433
69 New Zealand 153
19 Australia 1,007
41 Singapore 425
70 Slovakia 137
20 Nigeria 973
42 Ukraine 400
21 Taiwan 971
43 Kazakhstan 395
Total GDP** 95,569
22 Thailand 965
44 Chile 386
World GDP 102,255
* dropped from the analysis because of lack of data
** sum of the GDP of all economies in this study, excluding Iran and Iraq
28
Table 2 Measures and data sources
Dimension Measure Options for variables based on
qualitative assessment
Source
Education Literacy rates CIA World Factbook
Mean years of schooling Human Development
Report
Expected years of schooling Human Development
Report
Mean employment tenure short, medium, long
Main method of skills acquisition OJT, public vocational training,
private
Employment
relations
Union type company, party, industrial, craft
Union rights ITUC
Finance Main source of funding banks, markets
IFDI stock over GDP UNCTAD
Main criteria for allocation of
funds
state, relationships, market
Interfirm
relations
Number of M&A deals over
GDP, 2011-2013
Thomson One
Internal
dynamics
Internal decision-making
structure
top-down, participatory
Extent of delegation low, medium, high
Main criteria for pay raises and
promotions
seniority, performance,
relationships
Ownership
and
governance
Main ownership of large firms family, state, market (widely
held)
Main controlling owner family, state, market (widely
29
held)
Investor protection World Bank Doing
Business
Social capital Rule of law Worldwide
Governance Indicators
State Type developmental, predatory,
regulatory, welfare
Decision-making bottom-up, participatory
(corporatist), top-down
Voice and accountability Worldwide
Governance Indicators
Government effectiveness Worldwide
Governance Indicators
Regulatory quality Worldwide
Governance Indicators
Note: Taiwan data missing from international statistics completed using data from Taiwan National Statistics
30
Table 3 Pairwise institutional distances (Gower dissimilarity matrix), higher = less similar
DZ AR AU AT BD BE BR CA CL CN CO CU CZ DK EG FI FR DE GE HK HU IN ID
DZ Algeria
AR Argentina 0.18
AU Australia 0.45 0.41
AT Austria 0.54 0.46 0.54
BD Bangladesh 0.26 0.24 0.60 0.46
BE Belgium 0.56 0.43 0.52 0.04 0.48
BR Brazil 0.26 0.19 0.47 0.42 0.22 0.40
CA Canada 0.50 0.46 0.06 0.53 0.60 0.52 0.52
CL Chile 0.43 0.31 0.38 0.38 0.36 0.36 0.18 0.34
CN China 0.26 0.22 0.53 0.53 0.18 0.55 0.25 0.58 0.42
CO Colombia 0.20 0.10 0.39 0.44 0.26 0.41 0.12 0.44 0.24 0.23
CU Cuba 0.31 0.37 0.59 0.50 0.24 0.52 0.37 0.60 0.49 0.26 0.40
CZ Czech Republic 0.37 0.43 0.45 0.27 0.30 0.28 0.30 0.46 0.32 0.41 0.37 0.29
DK Denmark 0.58 0.45 0.46 0.13 0.60 0.13 0.46 0.41 0.38 0.66 0.43 0.59 0.39
EG Egypt 0.12 0.16 0.56 0.52 0.14 0.54 0.19 0.61 0.37 0.14 0.18 0.30 0.39 0.61
FI Finland 0.55 0.52 0.53 0.06 0.48 0.10 0.44 0.52 0.44 0.54 0.50 0.47 0.28 0.12 0.53
FR France 0.44 0.31 0.40 0.16 0.36 0.13 0.27 0.40 0.24 0.43 0.29 0.44 0.21 0.24 0.41 0.22
DE Germany 0.58 0.46 0.49 0.05 0.51 0.04 0.42 0.49 0.38 0.57 0.44 0.50 0.31 0.09 0.56 0.07 0.16
GE Greece 0.22 0.20 0.51 0.33 0.18 0.35 0.22 0.51 0.32 0.20 0.22 0.31 0.25 0.46 0.20 0.34 0.22 0.37
HK Hong Kong 0.33 0.21 0.27 0.43 0.44 0.39 0.27 0.31 0.24 0.37 0.18 0.57 0.42 0.40 0.35 0.47 0.27 0.43 0.31
HU Hungary 0.38 0.35 0.54 0.25 0.31 0.27 0.22 0.50 0.22 0.37 0.29 0.34 0.13 0.30 0.31 0.27 0.20 0.30 0.22 0.36
IN India 0.20 0.16 0.55 0.46 0.14 0.48 0.18 0.60 0.36 0.15 0.22 0.32 0.39 0.59 0.13 0.48 0.35 0.51 0.14 0.34 0.36
ID Indonesia 0.16 0.09 0.40 0.49 0.29 0.46 0.21 0.45 0.34 0.23 0.11 0.44 0.46 0.49 0.18 0.55 0.34 0.49 0.22 0.23 0.38 0.21
IE Ireland 0.50 0.46 0.16 0.44 0.51 0.41 0.47 0.17 0.38 0.53 0.39 0.51 0.31 0.42 0.57 0.45 0.30 0.39 0.42 0.40 0.44 0.55 0.45
DZ AR AU AT BD BE BR CA CL CN CO CU CZ DK EG FI FR DE GE HK HU IN ID
IL Israel 0.52 0.34 0.36 0.40 0.48 0.38 0.35 0.30 0.24 0.50 0.32 0.43 0.39 0.30 0.45 0.41 0.31 0.35 0.44 0.33 0.29 0.43 0.42
IT Italy 0.34 0.25 0.50 0.21 0.26 0.23 0.26 0.50 0.26 0.33 0.28 0.34 0.19 0.34 0.32 0.27 0.10 0.25 0.13 0.31 0.16 0.26 0.29
31
JP Japan 0.52 0.53 0.44 0.25 0.53 0.22 0.45 0.45 0.37 0.56 0.47 0.48 0.29 0.29 0.55 0.27 0.29 0.20 0.45 0.51 0.37 0.58 0.47
KZ Kazakhstan 0.22 0.17 0.52 0.43 0.18 0.40 0.16 0.57 0.33 0.16 0.15 0.26 0.31 0.52 0.15 0.45 0.32 0.43 0.15 0.31 0.28 0.16 0.19
KR Korea 0.36 0.33 0.38 0.43 0.36 0.41 0.26 0.34 0.20 0.38 0.25 0.40 0.31 0.37 0.34 0.45 0.28 0.38 0.32 0.32 0.26 0.40 0.26
KW Kuwait 0.29 0.28 0.43 0.39 0.31 0.40 0.20 0.48 0.37 0.28 0.21 0.35 0.31 0.43 0.27 0.40 0.28 0.43 0.26 0.36 0.23 0.32 0.25
MY Malaysia 0.20 0.15 0.45 0.54 0.25 0.52 0.21 0.49 0.35 0.18 0.15 0.39 0.43 0.53 0.14 0.55 0.38 0.55 0.23 0.28 0.36 0.21 0.11
MX Mexico 0.20 0.09 0.38 0.43 0.26 0.40 0.11 0.43 0.23 0.24 0.02 0.40 0.35 0.43 0.18 0.49 0.28 0.43 0.22 0.17 0.27 0.23 0.11
MA Morocco 0.19 0.21 0.50 0.55 0.15 0.57 0.22 0.56 0.40 0.20 0.22 0.37 0.38 0.59 0.12 0.57 0.45 0.60 0.29 0.39 0.35 0.22 0.17
NL Netherlands 0.60 0.56 0.39 0.15 0.61 0.14 0.53 0.39 0.44 0.68 0.55 0.51 0.32 0.17 0.66 0.15 0.26 0.11 0.48 0.51 0.40 0.61 0.60
NZ New Zealand 0.46 0.42 0.02 0.53 0.61 0.51 0.48 0.07 0.39 0.54 0.40 0.60 0.45 0.46 0.57 0.53 0.40 0.49 0.52 0.27 0.54 0.56 0.41
NG Nigeria 0.21 0.19 0.46 0.42 0.29 0.39 0.23 0.52 0.36 0.38 0.17 0.44 0.44 0.42 0.23 0.48 0.27 0.42 0.33 0.30 0.36 0.30 0.17
NO Norway 0.56 0.52 0.49 0.16 0.58 0.20 0.49 0.43 0.45 0.60 0.50 0.57 0.37 0.12 0.54 0.11 0.31 0.16 0.40 0.46 0.28 0.52 0.56
PK Pakistan 0.22 0.19 0.56 0.46 0.06 0.48 0.22 0.60 0.40 0.19 0.27 0.30 0.35 0.59 0.15 0.48 0.36 0.51 0.19 0.39 0.36 0.11 0.25
PE Peru 0.23 0.11 0.36 0.45 0.28 0.42 0.13 0.41 0.21 0.27 0.04 0.43 0.38 0.45 0.20 0.51 0.30 0.45 0.24 0.20 0.30 0.25 0.13
PH Philippines 0.15 0.10 0.40 0.49 0.30 0.47 0.22 0.45 0.34 0.22 0.11 0.43 0.46 0.49 0.17 0.55 0.35 0.49 0.21 0.24 0.38 0.21 0.02
PL Poland 0.25 0.22 0.39 0.45 0.32 0.44 0.27 0.44 0.38 0.33 0.21 0.47 0.43 0.45 0.26 0.47 0.36 0.45 0.30 0.31 0.39 0.29 0.21
PT Portugal 0.24 0.21 0.39 0.45 0.32 0.45 0.27 0.44 0.39 0.32 0.21 0.47 0.44 0.46 0.26 0.48 0.36 0.46 0.30 0.30 0.40 0.29 0.20
QA Qatar 0.24 0.21 0.39 0.46 0.31 0.45 0.27 0.45 0.40 0.32 0.20 0.48 0.45 0.46 0.25 0.48 0.37 0.47 0.30 0.30 0.41 0.28 0.19
RO Romania 0.23 0.20 0.39 0.46 0.31 0.46 0.26 0.45 0.40 0.31 0.20 0.48 0.46 0.47 0.25 0.49 0.38 0.48 0.30 0.30 0.42 0.27 0.18
RU Russia 0.22 0.19 0.38 0.47 0.31 0.47 0.26 0.45 0.41 0.31 0.19 0.49 0.47 0.47 0.24 0.50 0.38 0.48 0.30 0.29 0.42 0.27 0.17
SA Saudi Arabia 0.21 0.18 0.38 0.48 0.30 0.47 0.26 0.45 0.41 0.31 0.19 0.49 0.48 0.48 0.23 0.50 0.39 0.49 0.30 0.29 0.43 0.26 0.16
SG Singapore 0.20 0.18 0.38 0.48 0.30 0.48 0.26 0.45 0.42 0.30 0.18 0.50 0.48 0.48 0.23 0.51 0.39 0.50 0.30 0.29 0.44 0.26 0.16
SK Slovakia 0.19 0.17 0.37 0.49 0.30 0.49 0.26 0.45 0.43 0.30 0.18 0.50 0.49 0.49 0.22 0.52 0.40 0.51 0.30 0.28 0.45 0.25 0.15
ZA South Africa 0.18 0.16 0.37 0.49 0.29 0.49 0.26 0.45 0.43 0.30 0.17 0.51 0.50 0.50 0.22 0.52 0.41 0.51 0.30 0.28 0.46 0.25 0.14
DZ AR AU AT BD BE BR CA CL CN CO CU CZ DK EG FI FR DE GE HK HU IN ID
ES Spain 0.32 0.19 0.41 0.27 0.29 0.24 0.25 0.42 0.22 0.31 0.22 0.42 0.24 0.35 0.30 0.33 0.12 0.27 0.11 0.22 0.23 0.24 0.23
SE Sweden 0.61 0.48 0.45 0.12 0.58 0.11 0.49 0.44 0.45 0.60 0.46 0.57 0.38 0.13 0.59 0.11 0.22 0.08 0.40 0.42 0.37 0.53 0.52
CH Switzerland 0.64 0.51 0.43 0.16 0.61 0.15 0.48 0.38 0.34 0.63 0.50 0.60 0.41 0.09 0.62 0.15 0.27 0.12 0.47 0.37 0.30 0.61 0.55
TW Taiwan 0.32 0.24 0.42 0.42 0.32 0.39 0.25 0.37 0.18 0.34 0.22 0.45 0.35 0.37 0.30 0.48 0.27 0.42 0.28 0.27 0.24 0.36 0.17
TH Thailand 0.17 0.10 0.44 0.48 0.26 0.45 0.16 0.48 0.28 0.23 0.07 0.40 0.41 0.47 0.14 0.54 0.33 0.48 0.21 0.23 0.33 0.22 0.06
TR Turkey 0.39 0.29 0.39 0.43 0.26 0.41 0.21 0.34 0.14 0.37 0.19 0.40 0.31 0.43 0.32 0.49 0.28 0.43 0.30 0.31 0.23 0.36 0.30
32
UA Ukraine 0.11 0.10 0.45 0.45 0.20 0.47 0.19 0.50 0.35 0.19 0.13 0.33 0.37 0.49 0.13 0.46 0.34 0.49 0.13 0.25 0.29 0.14 0.12
AE UAE 0.30 0.30 0.42 0.38 0.32 0.40 0.22 0.47 0.36 0.29 0.21 0.37 0.31 0.42 0.28 0.40 0.27 0.42 0.26 0.35 0.24 0.33 0.27
GB UK 0.47 0.43 0.08 0.51 0.57 0.49 0.49 0.04 0.31 0.55 0.41 0.61 0.43 0.44 0.58 0.56 0.37 0.51 0.48 0.28 0.47 0.57 0.42
US United States 0.47 0.43 0.07 0.58 0.62 0.56 0.45 0.12 0.37 0.55 0.41 0.62 0.49 0.52 0.59 0.59 0.44 0.53 0.53 0.31 0.57 0.53 0.42
VE Venezuela 0.22 0.23 0.53 0.44 0.25 0.46 0.25 0.59 0.43 0.28 0.31 0.18 0.32 0.53 0.21 0.46 0.39 0.49 0.29 0.43 0.28 0.24 0.30
VN Vietnam 0.26 0.18 0.48 0.53 0.23 0.50 0.26 0.54 0.42 0.06 0.24 0.31 0.45 0.62 0.19 0.54 0.38 0.53 0.21 0.32 0.42 0.15 0.19
IE IL IT JP KZ KR KW MY MX MA NL NZ NG NO PK PE PH PL PT QA RO RU SA
IL Israel 0.36
IT Italy 0.40 0.39
JP Japan 0.30 0.48 0.33
KZ Kazakhstan 0.48 0.41 0.27 0.50
KR Korea 0.33 0.32 0.30 0.28 0.33
KW Kuwait 0.39 0.36 0.27 0.46 0.29 0.35
MY Malaysia 0.44 0.38 0.35 0.48 0.18 0.25 0.24
MX Mexico 0.39 0.31 0.27 0.46 0.16 0.26 0.20 0.16
MA Morocco 0.51 0.48 0.35 0.53 0.26 0.34 0.21 0.13 0.22
NL Netherlands 0.30 0.46 0.36 0.17 0.54 0.44 0.54 0.64 0.53 0.70
NZ New Zealand 0.15 0.36 0.50 0.44 0.53 0.39 0.44 0.45 0.39 0.51 0.39
NG Nigeria 0.51 0.40 0.31 0.49 0.28 0.33 0.22 0.27 0.17 0.24 0.52 0.47
NO Norway 0.44 0.37 0.36 0.36 0.50 0.44 0.45 0.55 0.50 0.62 0.20 0.48 0.53
PK Pakistan 0.56 0.53 0.26 0.58 0.19 0.41 0.31 0.26 0.27 0.15 0.62 0.56 0.25 0.57
PE Peru 0.36 0.34 0.29 0.43 0.17 0.28 0.22 0.18 0.03 0.24 0.51 0.37 0.20 0.52 0.29
PH Philippines 0.45 0.42 0.29 0.48 0.19 0.25 0.26 0.11 0.12 0.18 0.60 0.41 0.17 0.56 0.26 0.14
PL Poland 0.37 0.30 0.14 0.26 0.25 0.22 0.30 0.33 0.25 0.37 0.33 0.51 0.29 0.38 0.38 0.27 0.32
PT Portugal 0.30 0.38 0.12 0.38 0.23 0.29 0.36 0.29 0.22 0.34 0.41 0.40 0.35 0.41 0.26 0.24 0.24 0.18
QA Qatar 0.38 0.35 0.28 0.45 0.30 0.33 0.03 0.24 0.22 0.23 0.53 0.43 0.23 0.45 0.33 0.24 0.26 0.30 0.35
RO Romania 0.30 0.45 0.24 0.32 0.32 0.37 0.33 0.46 0.37 0.39 0.31 0.44 0.45 0.41 0.35 0.34 0.48 0.20 0.28 0.34
RU Russia 0.45 0.49 0.35 0.53 0.26 0.39 0.30 0.20 0.26 0.22 0.60 0.41 0.35 0.56 0.25 0.27 0.21 0.48 0.36 0.31 0.42
SA Saudi Arabia 0.43 0.31 0.36 0.54 0.25 0.28 0.19 0.17 0.20 0.21 0.67 0.48 0.34 0.49 0.29 0.22 0.23 0.38 0.35 0.19 0.41 0.23
SG Singapore 0.35 0.31 0.39 0.50 0.31 0.32 0.31 0.22 0.21 0.29 0.55 0.30 0.30 0.45 0.38 0.24 0.28 0.31 0.28 0.30 0.41 0.39 0.35
33
IE IL IT JP KZ KR KW MY MX MA NL NZ NG NO PK PE PH PL PT QA RO RU SA
SK Slovakia 0.28 0.27 0.18 0.35 0.26 0.33 0.31 0.39 0.26 0.42 0.32 0.42 0.34 0.34 0.39 0.28 0.37 0.11 0.15 0.31 0.21 0.47 0.39
ZA South Africa 0.29 0.18 0.28 0.45 0.30 0.25 0.26 0.29 0.16 0.37 0.47 0.34 0.29 0.38 0.42 0.18 0.27 0.27 0.25 0.27 0.40 0.38 0.20
ES Spain 0.31 0.35 0.09 0.34 0.21 0.26 0.35 0.28 0.21 0.38 0.37 0.41 0.34 0.38 0.29 0.23 0.23 0.16 0.04 0.34 0.31 0.33 0.34
SE Sweden 0.36 0.37 0.32 0.27 0.45 0.45 0.50 0.56 0.45 0.67 0.11 0.45 0.49 0.11 0.58 0.48 0.52 0.30 0.33 0.50 0.41 0.61 0.59
CH Switzerland 0.48 0.31 0.37 0.30 0.53 0.39 0.49 0.59 0.48 0.65 0.20 0.44 0.47 0.16 0.61 0.51 0.55 0.33 0.40 0.48 0.49 0.64 0.57
TW Taiwan 0.37 0.30 0.24 0.35 0.29 0.11 0.30 0.18 0.21 0.24 0.52 0.42 0.29 0.48 0.37 0.23 0.17 0.20 0.22 0.29 0.40 0.34 0.24
TH Thailand 0.44 0.36 0.28 0.46 0.15 0.25 0.25 0.10 0.07 0.17 0.59 0.44 0.18 0.55 0.26 0.09 0.07 0.26 0.23 0.27 0.42 0.25 0.23
TR Turkey 0.34 0.31 0.27 0.41 0.29 0.20 0.29 0.32 0.20 0.32 0.49 0.39 0.31 0.49 0.32 0.18 0.30 0.25 0.22 0.30 0.28 0.45 0.23
UA Ukraine 0.50 0.43 0.25 0.57 0.13 0.35 0.22 0.16 0.13 0.19 0.60 0.46 0.22 0.47 0.17 0.15 0.12 0.36 0.25 0.23 0.39 0.15 0.20
AE UAE 0.38 0.35 0.28 0.45 0.30 0.33 0.03 0.23 0.22 0.23 0.53 0.43 0.23 0.45 0.33 0.24 0.27 0.30 0.35 0.01 0.34 0.32 0.19
GB UK 0.18 0.33 0.47 0.46 0.54 0.35 0.45 0.46 0.40 0.52 0.42 0.09 0.48 0.47 0.57 0.38 0.42 0.47 0.37 0.44 0.40 0.44 0.40
US United States 0.21 0.34 0.53 0.48 0.54 0.40 0.45 0.47 0.40 0.53 0.45 0.08 0.49 0.55 0.57 0.38 0.43 0.53 0.43 0.45 0.46 0.45 0.49
VE Venezuela 0.58 0.42 0.29 0.56 0.24 0.42 0.25 0.32 0.30 0.27 0.55 0.54 0.29 0.51 0.20 0.33 0.31 0.36 0.37 0.27 0.33 0.23 0.29
VN Vietnam 0.53 0.51 0.33 0.56 0.16 0.38 0.34 0.18 0.25 0.25 0.63 0.49 0.32 0.60 0.19 0.27 0.18 0.40 0.28 0.34 0.47 0.18 0.26
SG SK ZA ES SE CH TW TH TR UA AE GB US VE
SK Slovakia 0.28
ZA South Africa 0.25 0.21
ES Spain 0.30 0.16 0.23
SE Sweden 0.45 0.25 0.39 0.29
CH Switzerland 0.44 0.33 0.39 0.37 0.16
TW Taiwan 0.26 0.31 0.19 0.20 0.49 0.43
TH Thailand 0.22 0.31 0.21 0.22 0.51 0.54 0.16
TR Turkey 0.30 0.26 0.16 0.26 0.50 0.44 0.21 0.25
UA Ukraine 0.28 0.37 0.28 0.23 0.52 0.55 0.27 0.13 0.31
AE UAE 0.30 0.31 0.27 0.34 0.50 0.48 0.30 0.27 0.30 0.24
GB UK 0.31 0.40 0.27 0.39 0.47 0.41 0.34 0.45 0.31 0.47 0.44
US United States 0.36 0.46 0.37 0.45 0.51 0.50 0.45 0.46 0.41 0.47 0.45 0.14
34
VE Venezuela 0.46 0.40 0.40 0.36 0.56 0.54 0.40 0.31 0.36 0.21 0.28 0.55 0.57
VN Vietnam 0.41 0.41 0.40 0.27 0.56 0.58 0.35 0.24 0.38 0.18 0.34 0.50 0.51 0.28
35
Table 4 Clusters of business systems among the world’s major economies
Socialist
Economies
Emerging
Economies
Arab Oil-
Based
Economies
Advanced
City
Economies
Advanced
Emerging
Economies
European
Peripheral
Economies
Liberal
Market
Economies
Coordinated
Market
Economies
Collaborative
Economies
Cuba Algeria Kuwait Hong Kong Chile Czech Rep. Australia Austria Japan
Venezuela Argentina Qatar Singapore Israel France Canada Belgium
Bangladesh S. Arabia Korea Greece Ireland Denmark
Brazil UAE S. Africa Hungary N. Zealand Finland
China Taiwan Italy UK Germany
Colombia Turkey Poland US Netherlands
Egypt Portugal Norway
India Romania Sweden
Indonesia Slovakia Switzerland
Kazakhstan Spain
Malaysia
Mexico
Morocco
Nigeria
Pakistan
Peru
Philippines
Russia
Thailand
Ukraine
Vietnam
